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Unmanned Aerial Vehicle for Rescue and Triage

  • Darwin Armando Mora Arias
  • Juan Carlos Ortega Castro
  • Carlos Flores-Vázquez
  • Daniel Icaza
  • Juan-Carlos Cobos-TorresEmail author
Conference paper
  • 48 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

In recent years, the rescue of natural disaster victims has included the support of robotic systems to search for trapped people. However, the victims that are found by robots do not have their vital signs evaluated until the rescue team reaches their location. This can complicate matters in difficult-to-access locations and places affected by toxic waste or radiation, where the physical integrity of rescue teams is at risk. This research proposes the use of an unmanned aerial vehicle in the search for victims and performing basic triage (heart and respiratory rate measurement) through a contactless method to support rescue efforts. The main contribution is a decrease in response time in case of a search-and-rescue emergency. The system consists of navigating over a certain area designated as the disaster zone for the search of possible disaster victims that are lying on the ground. Once the victim is located, the navigation is reprogrammed to carry out the search and face recognition. Finally, by automatically selecting a skin area, the heart and respiratory rates are measured. The measurement is carried out through the photoplethysmography imaging technique, without any contact sensor. The comparison of the basic triage results with and without contact confirms to us the efficacy of the proposed method. The Bland-Altman data analysis shows a close correlation of heart and respiratory rates measured with both approaches (correlation coefficient of 0.90 for heart rate and 0.84 for respiratory rate).

Keywords

Recue UAV Triage UAV Search and rescue Vital signs Photoplethysmography imaging 

Notes

Acknowledgments

The research leading to these results has received funding from SmartUniverCity 2.0 program, funded by “Optimización energética del sistema de recaudo en Unidades de Transporte Urbano”.

References

  1. 1.
    Demoraes, F., D’Ercole, R.: Cartografía de las amenazas de origen natural por cantón en Ecuador (2001)Google Scholar
  2. 2.
    Lara Calderon, M.L.: Consecuencias de lecciones no aprendidas que se evidenciaron tras el Terremoto del 16 de abril 2016. TABUGA, comunidad rural ubicada cerca al epicentro (2017)Google Scholar
  3. 3.
    Correa, A.C.: Sistemas robóticos teleoperados. Ciencia E Ingeniería Neogranadina (15), 62–69 (2005)Google Scholar
  4. 4.
    Escobar, J.J..J.M., Ramírez, E.A.D., Hernández, J.C., Matamoros, O.M., Padilla, R.T.: White-donkey: búsqueda de personas con vehículos aéreos no tripulados basada en visión por computadora, vol. 120, pp. 53–63 (2016)Google Scholar
  5. 5.
    Beltrán Huertas, D.C., Bonilla Guerra, J.S.: Robot bio-inspirado para asistencia de búsqueda en situaciones de colapsos estructurales (2018)Google Scholar
  6. 6.
    Bermudez, G., Novoa, K.S., Infante, W.: La robótica en actividades de búsqueda y rescate urbano. Origen, actualidad y perspectivas. Tecnura 8(15), 97–108 (2004)Google Scholar
  7. 7.
    González, R.C.R.: Triage en Emergencias Extrahospitalarias. Monografía de Investigación En Salud. Sevilla: Fundacion INDEX (2014)Google Scholar
  8. 8.
    Arai, M., Tanaka, Y., Hirose, S., Kuwahara, H., Tsukui, S.: Development of ‘Souryu-IV’ and ‘Souryu-V:’ serially connected crawler vehicles for in-rubble searching operations. J. Field Robot. 25(1–2), 31–65 (2008)CrossRefGoogle Scholar
  9. 9.
    Park, J., Lee, G., Park, J.: Infrared image based human victim recognition for a search and rescue robot. J. Inst. Control Robot. Syst. 22(4), 288–292 (2016)CrossRefGoogle Scholar
  10. 10.
    Henderson, R.M.: Congratulations to the Spring 2008 MTT-S undergraduate/pre-graduate scholarship awardees! [Education News]. IEEE Microwave Mag. 9(6), 187–190 (2008).  https://doi.org/10.1109/mmm.2008.929730CrossRefGoogle Scholar
  11. 11.
    Tsai, T.-H., Shiu, B.-Y., Ho, C.-L., Lin, J.: A vital sign radar receiver with integrated A/D converter and dynamic clutter cancellation. Presented at the 2016 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), pp. 1–3 (2016).  https://doi.org/10.1109/rfit.2016.7578142
  12. 12.
    Rahman, H., Ahmed, M.U., Begum, S.: Non-contact heart rate monitoring using lab color space. In: pHealth (2016)Google Scholar
  13. 13.
    Portilla, K., Santos, V., Trujillo, M.F., Rosales, A.: Non-invasive heart rate monitor applying independent component analysis in videos. Presented at the 2017 International Conference on Information Systems and Computer Science (INCISCOS), pp. 121–127 (2017)Google Scholar
  14. 14.
    Cobos-Torres, J.C., Abderrahim, M.: Measuring heart and breath rates by image photoplethysmography using wavelets technique. IEEE Lat. Am. Trans. 15(10), 1864–1868 (2017).  https://doi.org/10.1109/TLA.2017.8071228CrossRefGoogle Scholar
  15. 15.
    Sotelo, V.R.B., Sánchez, J.R.G., Ortigoza, R.S.: Robots Móviles: Evolución y Estado del Arte. Polibits (35), 12–17 (2007)Google Scholar
  16. 16.
    Sobti, A., Arora, C., Balakrishnan, M.: Object detection in real-time systems: going beyond precision. Presented at the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1020–1028 (2018).  https://doi.org/10.1109/wacv.2018.00117
  17. 17.
    Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)Google Scholar
  18. 18.
    Rajnathsing, H., Li, C.: A neural network based monitoring system for safety in shared work-space human-robot collaboration. Ind. Robot: Int. J. 45(4), 481–491 (2018)CrossRefGoogle Scholar
  19. 19.
    Carrasco, J.L., Jover, L.: Métodos estadísticos para evaluar la concordancia. Med. Clin. 122(1), 28–34 (2004)CrossRefGoogle Scholar
  20. 20.
    Lubecke, V.M., Boric-Lubecke, O., Host-Madsen, A., Fathy, A.E.: Through-the-wall radar life detection and monitoring, pp. 769–772 (2007).  https://doi.org/10.1109/mwsym.2007.380053

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.TelComSistema Cia. Ltda.CuencaEcuador
  2. 2.Catholic University of CuencaCuencaEcuador

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